version 0.8.3

DESCRIPTION

@@ -2,14 +2,14 @@ Package: worldmet
 Type: Package
 Title: Import Surface Meteorological Data from NOAA Integrated Surface
         Database (ISD)
-Version: 0.8.0
-Date: 2017-12-15
+Version: 0.8.3
+Date: 2018-02-08
 Authors@R: c(person("David", "Carslaw", role = c("aut", "cre"), email =
     "david.carslaw@york.ac.uk"))
 ByteCompile: true
 Depends: R (>= 3.2.0)
-Imports: openair, doParallel, parallel, foreach, plyr, dplyr, leaflet,
-        readr, zoo
+Imports: openair, doParallel, parallel, foreach, dplyr, leaflet, readr,
+        zoo
 Maintainer: David Carslaw <david.carslaw@york.ac.uk>
 Description: Functions to import data from more than 30,000 surface
     meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface
@@ -21,7 +21,7 @@ LazyLoad: true
 LazyData: true
 RoxygenNote: 6.0.1
 NeedsCompilation: no
-Packaged: 2017-12-18 15:29:15 UTC; David
+Packaged: 2018-02-16 12:02:49 UTC; David
 Author: David Carslaw [aut, cre]
 Repository: CRAN
-Date/Publication: 2017-12-18 15:40:33 UTC
+Date/Publication: 2018-02-16 12:42:06 UTC

MD5

@@ -1,19 +1,19 @@
-09f9bdf63a073ecde8cf0f31a8145e78 *DESCRIPTION
-2dbbc4f80734d2ebbafc11750ff7eb73 *NAMESPACE
-c907682c4ac72c8f0ae11146430a592a *R/exportADMS.R
-20fc7ad7978a1160a351139d461fd0f1 *R/getMeta.R
-3e3d2d2964be0fd5e9b18bce78c1c490 *R/metNOAA.R
-14fcd05358778bdc3207bed2db1ddc83 *R/meta.R
-966fbfcf2da1001f070f1107e4ec0686 *R/sysdata.rda
-cf4532f8d10db6d94136193ccf0f1cdb *R/weatherCodes.R
-1ff037620039bef68eeeab3e4a5e68d1 *R/worldmet-package.R
+9815f1fa5ba4ad3b3805b63a2d3c1cba *DESCRIPTION
+715f470e8b2c21b692c683a516b66576 *NAMESPACE
+fde382d0eda08d1f08aaffa5bb40c92f *R/exportADMS.R
+932748d6470eb3d6d84de172bfa4d141 *R/getMeta.R
+504b61ccbae9316f503501b53f5c048f *R/metNOAA.R
+b15a961f15873be7d691ca7eb1dffebf *R/meta.R
+7cfccd5e8a36ebd23b3d3b7406507524 *R/sysdata.rda
+072fafd8f8db6bf7a3138ea8a2735b16 *R/weatherCodes.R
+b0ee29b89da7fa78035a2c171aca205c *R/worldmet-package.R
 cc167598e5b17a10afb4d83e73e8a827 *README.md
-966fbfcf2da1001f070f1107e4ec0686 *data/meta.rda
+7cfccd5e8a36ebd23b3d3b7406507524 *data/meta.rda
 8699a0e8e38705634dd0ae8f861ea0c4 *data/weatherCodes.rda
 84f21841c2bbb1c09c3ffbcf11ec2145 *inst/images/map.PNG
-ffaf8662444fd21bd33c21c40e7cb9f6 *man/exportADMS.Rd
-1018f48bb736b43d53e7ab416cbc8aff *man/getMeta.Rd
-f6d3bcadfbf98522f4b5370b11912bc0 *man/importNOAA.Rd
+6fbcf4883b82a1b0bfa61465ce32f1ac *man/exportADMS.Rd
+9fa5c61807f1f86079eb52f699576354 *man/getMeta.Rd
+fa32bfe9485a9ca294867042d138d111 *man/importNOAA.Rd
 f2cefd98affecb5b3afaf90b79db7036 *man/meta.Rd
 ec57f553d8801d0013614cbc37a6f6c7 *man/weatherCodes.Rd
 6ab42224111ef0481fb97bb9eb324d79 *man/worldmet.Rd

NAMESPACE

@@ -4,12 +4,11 @@ export(exportADMS)
 export(getMeta)
 export(importNOAA)
 import(doParallel)
+import(dplyr)
 import(foreach)
 import(openair)
 import(parallel)
-import(plyr)
 import(readr)
-importFrom(dplyr,"%>%")
 importFrom(leaflet,addCircles)
 importFrom(leaflet,addMarkers)
 importFrom(leaflet,addTiles)

R/exportADMS.R

@@ -1,109 +1,117 @@
 #' Export a meteorological data frame in ADMS format
-#' 
+#'
 #' @param dat A data frame imported by \code{\link{importNOAA}}.
-#' @param out A file name for the ADMS file. The file is written to the working 
+#' @param out A file name for the ADMS file. The file is written to the working
 #'   directory by default.
-#' @param interp Should interpolation of missing values be undertaken? If 
-#'   \code{TRUE} linear interpolation is carried out for gaps of up to and 
+#' @param interp Should interpolation of missing values be undertaken? If
+#'   \code{TRUE} linear interpolation is carried out for gaps of up to and
 #'   including \code{maxgap}.
-#' @param maxgap The maximum gap in hours that should be interpolated where 
+#' @param maxgap The maximum gap in hours that should be interpolated where
 #'   there are missing data when \code{interp = TRUE.} Data with gaps more than
 #'   \code{maxgap} are left as missing.
-#'   
+#'
 #' @return Writes a text file to a location of the user's choosing.
 #' @export
 #' @importFrom zoo na.approx
-#' @examples 
-#' 
+#' @examples
+#'
 #' \dontrun{
 #' ## import some data then export it
 #' dat <- importNOAA(year = 2012)
 #' exportADMS(dat, file = "~/temp/adms_met.MET")
 #' }
 exportADMS <- function(dat, out = "./ADMS_met.MET", interp = FALSE, maxgap = 2) {
-  
+
   # keep R check quiet
-  wd = u = v = NULL
-
-    ## make sure the data do not have gaps
-    all.dates <- data.frame(
-        date = seq(ISOdatetime(year = as.numeric(format(dat$date[1], "%Y")),
-                               month = 1, day = 1, hour = 0, min = 0,
-                               sec = 0, tz = "GMT"),
-                   ISOdatetime(year = as.numeric(format(dat$date[1], "%Y")), 
-                               month = 12, day = 31, hour = 23, min = 0, 
-                               sec = 0, tz = "GMT"), by = "hour")
+  wd <- u <- v <- NULL
+
+  ## make sure the data do not have gaps
+  all.dates <- data.frame(
+    date = seq(ISOdatetime(
+      year = as.numeric(format(dat$date[1], "%Y")),
+      month = 1, day = 1, hour = 0, min = 0,
+      sec = 0, tz = "GMT"
+    ),
+    ISOdatetime(
+      year = as.numeric(format(dat$date[1], "%Y")),
+      month = 12, day = 31, hour = 23, min = 0,
+      sec = 0, tz = "GMT"
+    ),
+    by = "hour"
     )
-
-    dat <- merge(dat, all.dates, all = TRUE)
-
-    ## make sure precipitation is available
-    if (!"precip" %in% names(dat))
-        dat$precip <- NA
-
-    if (interp) {
-
-        ## variables to interpolate
-        ## note need to deal with wd properly
-        dat <- transform(dat, u = sin(pi * wd / 180), v = cos(pi * wd / 180))
-
-        varInterp <- c("ws", "u", "v", "air_temp", "RH", "cl", "precip")
-
-        # don't want to try and interpret fields that are all missing
-        ids <- sapply(varInterp, function (x) !all(is.na(dat[[x]])))
-        varInterp <- varInterp[ids]
-
-        dat[varInterp] <- zoo::na.approx(dat[varInterp], maxgap = maxgap, na.rm = FALSE)
-
-        ## now put wd back
-        dat <- transform(dat, wd = as.vector(atan2(u, v) * 360 / 2 / pi))
-
-        ## correct for negative wind directions
-        ids <- which(dat$wd < 0)  ## ids where wd < 0
-        dat$wd[ids] <- dat$wd[ids] + 360
-    }
-
-    ## exports met data to ADMS format file
-    year <- as.numeric(format(dat$date, "%Y"))
-    day <- as.numeric(format(dat$date, "%j"))
-    hour <- as.numeric(format(dat$date, "%H"))
-    station <- "0000"
-
-    ## data frame of met data needed
-    adms <- data.frame(station, year, day, hour,
-                       round(dat$air_temp, 1), round(dat$ws, 1),
-                       round(dat$wd, 1), round(dat$RH, 1),
-                       round(dat$cl), round(dat$precip, 1),
-                       stringsAsFactors = FALSE)
-
-    ## print key data capture rates to the screen
-    dc <- round(100 - 100 * (length(which(is.na(dat$ws))) / length(dat$ws)), 1)
-    print(paste("Data capture for wind speed:", dc, "%"))
-
-    dc <- round(100 - 100 * (length(which(is.na(dat$wd))) / length(dat$wd)), 1)
-    print(paste("Data capture for wind direction:", dc, "%"))
-
-    dc <- round(100 - 100 * (length(which(is.na(dat$air_temp))) / length(dat$air_temp)), 1)
-    print(paste("Data capture for temperature:", dc, "%"))
-
-    dc <- round(100 - 100 * (length(which(is.na(dat$cl))) / length(dat$cl)), 1)
-    print(paste("Data capture for cloud cover:", dc, "%"))
-
-    ## replace NA with -999
-    adms[] <- lapply(adms, function(x) replace(x, is.na(x), -999))
-
-    ## write the data file
-    write.table(adms, file = out, col.names = FALSE, row.names = FALSE, 
-                sep = ",", quote = FALSE)
-
-    ## add the header lines
-    fConn <- file(out, 'r+') 
-    Lines <- readLines(fConn) 
-    writeLines(c("VARIABLES:\n10\nSTATION DCNN\nYEAR\nTDAY\nTHOUR\nT0C\nU\nPHI\nRHUM\nCL\nP\nDATA:",
-                 Lines), con = fConn) 
-    close(fConn) 
-
-}
+  )
+
+  dat <- merge(dat, all.dates, all = TRUE)
+
+  ## make sure precipitation is available
+  if (!"precip" %in% names(dat)) {
+    dat$precip <- NA
+  }
+
+  if (interp) {
+
+    ## variables to interpolate
+    ## note need to deal with wd properly
+    dat <- transform(dat, u = sin(pi * wd / 180), v = cos(pi * wd / 180))
+
+    varInterp <- c("ws", "u", "v", "air_temp", "RH", "cl", "precip")
+
+    # don't want to try and interpret fields that are all missing
+    ids <- sapply(varInterp, function(x) !all(is.na(dat[[x]])))
+    varInterp <- varInterp[ids]
+
+    dat[varInterp] <- zoo::na.approx(dat[varInterp], maxgap = maxgap, na.rm = FALSE)
 
+    ## now put wd back
+    dat <- transform(dat, wd = as.vector(atan2(u, v) * 360 / 2 / pi))
 
+    ## correct for negative wind directions
+    ids <- which(dat$wd < 0) ## ids where wd < 0
+    dat$wd[ids] <- dat$wd[ids] + 360
+  }
 
+  ## exports met data to ADMS format file
+  year <- as.numeric(format(dat$date, "%Y"))
+  day <- as.numeric(format(dat$date, "%j"))
+  hour <- as.numeric(format(dat$date, "%H"))
+  station <- "0000"
+
+  ## data frame of met data needed
+  adms <- data.frame(station, year, day, hour,
+    round(dat$air_temp, 1), round(dat$ws, 1),
+    round(dat$wd, 1), round(dat$RH, 1),
+    round(dat$cl), round(dat$precip, 1),
+    stringsAsFactors = FALSE
+  )
+
+  ## print key data capture rates to the screen
+  dc <- round(100 - 100 * (length(which(is.na(dat$ws))) / length(dat$ws)), 1)
+  print(paste("Data capture for wind speed:", dc, "%"))
+
+  dc <- round(100 - 100 * (length(which(is.na(dat$wd))) / length(dat$wd)), 1)
+  print(paste("Data capture for wind direction:", dc, "%"))
+
+  dc <- round(100 - 100 * (length(which(is.na(dat$air_temp))) / length(dat$air_temp)), 1)
+  print(paste("Data capture for temperature:", dc, "%"))
+
+  dc <- round(100 - 100 * (length(which(is.na(dat$cl))) / length(dat$cl)), 1)
+  print(paste("Data capture for cloud cover:", dc, "%"))
+
+  ## replace NA with -999
+  adms[] <- lapply(adms, function(x) replace(x, is.na(x), -999))
+
+  ## write the data file
+  write.table(adms,
+    file = out, col.names = FALSE, row.names = FALSE,
+    sep = ",", quote = FALSE
+  )
+
+  ## add the header lines
+  fConn <- file(out, "r+")
+  Lines <- readLines(fConn)
+  writeLines(c(
+    "VARIABLES:\n10\nSTATION DCNN\nYEAR\nTDAY\nTHOUR\nT0C\nU\nPHI\nRHUM\nCL\nP\nDATA:",
+    Lines
+  ), con = fConn)
+  close(fConn)
+}